AutoML [1]}, focuses on developing approaches that provide efficient methods to design machine learning workflows without extensive need for human intervention or optimization processes [2]}. There have been proposals for solving the optimization problem of designing machine learning workflows using random search [3]}, evolutionary strategies [4]}, bayesian optimization [5]} and reinforcement learning [6]}. In [7]}, the authors extended Auto-Weka in order to use AutoML to design methods to detect railway track defects. In [8]}, AutoML is leveraged to improve the classification component of CNNs. This method was built by training CNNs until convergence and then partially removing their classification component and replacing it by a searched model, resulting in performance improvements both in accuracy and inference time. Finally, in [9]}, AutoML is used to forecasting bank failures by performing automated feature extraction.
